The invention pertains to machine vision and, more particularly, to methods for inspection of the surfaces of semiconductor dies.
At the heart of an integrated circuit is a semiconductor die. This is a wafer of semiconducting material (e.g., silicon) with hundreds of thousands or millions of electronic circuit components etched into its layers. To enhance processing speed and reduce power consumption, the dies are made as small as possible, e.g., less than a square-inch in area and several mils thick. To facilitate handling, the dies are glued into supporting frames, i.e., lead frames. In addition to providing stability, these frames have large conductive leads that can be soldered to other circuit components, e.g., on a printed circuit board. The leads are typically connected to corresponding pads on the die via a process called wire bonding, wherein a small conductive thread is bonded to each lead and its corresponding pad. Once a semiconductor die and its frame are assembled, they are typically packaged in a ceramic or plastic, forming an integrated circuit.
It is important to inspect the surface of the semiconductor die after die bonding. The most common defect is the deposit of unwanted adhesive on the die. Since the adhesive is conductive, it can effectively "short circuit" the semiconductor die's electronic functions.
The inspection of semiconductor dies for adhesive has proven to be a vexing machine vision problem. This is a result of the complexity of the "background," i.e., the circuitry pattern etched into the layers of the wafer.
The prior art suggests the use of a technique referred to golden template comparison (GTC) to inspect die surfaces. GTC is a technique for locating objects by comparing a feature under scrutiny (to wit, a die surface) to a good image--or golden template--that is stored in memory. The technique subtracts the good image from the test image and analyzes the difference to determine if the expected object (e.g., a defect) is present. For example, upon subtracting the image of a good die surface from a defective one, the resulting "difference" image would reveal an adhesive blotch that could be flagged as a defect.
Before GTC inspections can be performed, the system must be "trained" so that the golden template can be stored in memory. To this end, the GTC training functions are employed to analyze several good samples of a scene to create a "mean" image and "standard deviation" image. The mean image is a statistical average of all the samples analyzed by the training functions. It defines what a typical good scene looks like. The standard deviation image defines those areas on the object where there is little variation from part to part, as well as those areas in which there is great variation from part to part. This latter image permits GTC's runtime inspection functions to use less sensitivity in areas of greater expected variation, and more sensitivity in areas of less expected variation.
At runtime, a system employing GTC captures an image of a scene of interest. Where the position of that scene is different from the training position, the captured image is aligned, or registered, with the mean image. The intensities of the captured image are also normalized with those of the mean image to ensure that variations illumination do not adversely affect the comparison.
The GTC inspection functions then subtract the registered, normalized, captured image from the mean image to produce a difference image that contains all the variations between the two. That difference image is then compared with a "threshold" image derived from the standard deviation image. This determines which pixels of the difference image are to be ignored and which should be analyzed as possible defects. The latter are subjected to morphology, to eliminate or accentuate pixel data patterns and to eliminate noise. An object recognition technique, such as connectivity analysis, can then be employed to classify the apparent defects.
Although GTC inspection tools have proven quite successful, they suffer some limitations. For example, except in unusual circumstances, GTC requires registration--i.e., that the image under inspection be registered with the template image. GTC also uses a standard deviation image for thresholding, which can result in a loss of resolution near edges due to high resulting threshold values. GTC is, additionally, limited to applications where the images are repeatable: it cannot be used where image-to-image variation results form changes in size, shape, orientation and warping.
In application to die surface inspection, GTC is limited because its fixed template typically does not include the die edges which do not tend to be repeatable from die-to-die, due to manufacturing machinery sawing tolerances. It is here, however, that the probability of deposited adhesive is very high. Moreover, the complexity of the etching patterns on the die surfaces results in a large area being effectively masked by the high standard deviation.
An object of this invention, therefore, is to provide improved methods for machine vision and, more particularly, improved methods for inspecting the surfaces of semiconductor dies.
A further object is to provide such methods that can be used to identify defects such as adhesive blotches on those surfaces.
Yet another object is to provide such methods that do not routinely necessitate alignment or registration of an image under inspection with a template image.
Still yet a further object of the invention is to provide such methods that do not require training.
Still other objects of the invention include providing such machine vision methods as can be readily implemented on existing machine vision processing equipment, and which can be implemented for rapid execution without excessive consumption of computational power.